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Abstract

This paper presents a meta-heuristic algorithm for
solving the Flexible Job Shop Scheduling Problem
(FJSSP). This strategy, known as Iterative Flatten-ing Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are
randomly retracted from the current solution; and a
solving-step, in which a new solution is incremen-tally recomputed from this partial schedule. This
work contributes two separate results: (1) it pro-poses a constraint-based procedure extending an
existing approach previously used for classical Job
Shop Scheduling Problem; (2) it proposes an origi-nal relaxation strategy on feasible FJSSP solutions
based on the idea of randomly breaking the exe-cution orders of the activities on the machines and
opening the resource options for some activities se-lected at random. The efﬁcacy of the overall heuris-tic optimization algorithm is demonstrated on a set
of well-known benchmarks.